mirror of
https://github.com/VinciGit00/Scrapegraph-ai.git
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245 lines
8.7 KiB
Python
245 lines
8.7 KiB
Python
"""
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BaseNode Module
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"""
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import re
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from abc import ABC, abstractmethod
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from typing import List, Optional
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from ..utils import get_logger
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class BaseNode(ABC):
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"""
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An abstract base class for nodes in a graph-based workflow, designed to perform specific actions when executed.
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Attributes:
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node_name (str): The unique identifier name for the node.
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input (str): Boolean expression defining the input keys needed from the state.
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output (List[str]): List of
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min_input_len (int): Minimum required number of input keys.
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node_config (Optional[dict]): Additional configuration for the node.
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logger (logging.Logger): The centralized root logger
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Args:
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node_name (str): Name for identifying the node.
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node_type (str): Type of the node; must be 'node' or 'conditional_node'.
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input (str): Expression defining the input keys needed from the state.
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output (List[str]): List of output keys to be updated in the state.
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min_input_len (int, optional): Minimum required number of input keys; defaults to 1.
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node_config (Optional[dict], optional): Additional configuration for the node; defaults to None.
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Raises:
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ValueError: If `node_type` is not one of the allowed types.
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Example:
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>>> class MyNode(BaseNode):
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... def execute(self, state):
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... # Implementation of node logic here
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... return state
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...
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>>> my_node = MyNode("ExampleNode", "node", "input_spec", ["output_spec"])
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>>> updated_state = my_node.execute({'key': 'value'})
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{'key': 'value'}
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"""
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def __init__(
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self,
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node_name: str,
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node_type: str,
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input: str,
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output: List[str],
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min_input_len: int = 1,
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node_config: Optional[dict] = None,
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):
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self.node_name = node_name
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self.input = input
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self.output = output
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self.min_input_len = min_input_len
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self.node_config = node_config
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self.logger = get_logger()
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if node_type not in ["node", "conditional_node"]:
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raise ValueError(
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f"node_type must be 'node' or 'conditional_node', got '{node_type}'"
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)
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self.node_type = node_type
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@abstractmethod
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def execute(self, state: dict) -> dict:
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"""
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Execute the node's logic based on the current state and update it accordingly.
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Args:
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state (dict): The current state of the graph.
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Returns:
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dict: The updated state after executing the node's logic.
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"""
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pass
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def update_config(self, params: dict, overwrite: bool = False):
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"""
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Updates the node_config dictionary as well as attributes with same key.
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Args:
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param (dict): The dictionary to update node_config with.
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overwrite (bool): Flag indicating if the values of node_config should be overwritten if their value is not None.
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"""
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if self.node_config is None:
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self.node_config = {}
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for key, val in params.items():
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if hasattr(self, key) and (key not in self.node_config or overwrite):
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self.node_config[key] = val
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setattr(self, key, val)
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def get_input_keys(self, state: dict) -> List[str]:
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"""
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Determines the necessary state keys based on the input specification.
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Args:
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state (dict): The current state of the graph used to parse input keys.
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Returns:
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List[str]: A list of input keys required for node operation.
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Raises:
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ValueError: If error occurs in parsing input keys.
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"""
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try:
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input_keys = self._parse_input_keys(state, self.input)
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self._validate_input_keys(input_keys)
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return input_keys
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except ValueError as e:
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raise ValueError(f"Error parsing input keys for {self.node_name}: {str(e)}")
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def _validate_input_keys(self, input_keys):
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"""
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Validates if the provided input keys meet the minimum length requirement.
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Args:
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input_keys (List[str]): The list of input keys to validate.
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Raises:
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ValueError: If the number of input keys is less than the minimum required.
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"""
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if len(input_keys) < self.min_input_len:
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raise ValueError(
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f"""{self.node_name} requires at least {self.min_input_len} input keys,
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got {len(input_keys)}."""
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)
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def _parse_input_keys(self, state: dict, expression: str) -> List[str]:
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"""
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Parses the input keys expression to extract relevant keys from the state based on logical conditions.
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The expression can contain AND (&), OR (|), and parentheses to group conditions.
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Args:
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state (dict): The current state of the graph.
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expression (str): The input keys expression to parse.
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Returns:
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List[str]: A list of key names that match the input keys expression logic.
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Raises:
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ValueError: If the expression is invalid or if no state keys match the expression.
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"""
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# Check for empty expression
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if not expression:
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raise ValueError("Empty expression.")
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# Check for adjacent state keys without an operator between them
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pattern = (
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r"\b("
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+ "|".join(re.escape(key) for key in state.keys())
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+ r")(\b\s*\b)("
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+ "|".join(re.escape(key) for key in state.keys())
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+ r")\b"
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)
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if re.search(pattern, expression):
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raise ValueError(
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"Adjacent state keys found without an operator between them."
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)
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# Remove spaces
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expression = expression.replace(" ", "")
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# Check for operators with empty adjacent tokens or at the start/end
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if (
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expression[0] in "&|"
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or expression[-1] in "&|"
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or "&&" in expression
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or "||" in expression
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or "&|" in expression
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or "|&" in expression
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):
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raise ValueError("Invalid operator usage.")
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# Check for balanced parentheses and valid operator placement
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open_parentheses = close_parentheses = 0
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for i, char in enumerate(expression):
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if char == "(":
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open_parentheses += 1
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elif char == ")":
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close_parentheses += 1
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# Check for invalid operator sequences
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if char in "&|" and i + 1 < len(expression) and expression[i + 1] in "&|":
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raise ValueError(
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"Invalid operator placement: operators cannot be adjacent."
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)
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# Check for missing or balanced parentheses
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if open_parentheses != close_parentheses:
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raise ValueError("Missing or unbalanced parentheses in expression.")
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# Helper function to evaluate an expression without parentheses
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def evaluate_simple_expression(exp: str) -> List[str]:
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"""Evaluate an expression without parentheses."""
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# Split the expression by the OR operator and process each segment
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for or_segment in exp.split("|"):
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# Check if all elements in an AND segment are in state
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and_segment = or_segment.split("&")
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if all(elem.strip() in state for elem in and_segment):
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return [
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elem.strip() for elem in and_segment if elem.strip() in state
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]
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return []
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# Helper function to evaluate expressions with parentheses
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def evaluate_expression(expression: str) -> List[str]:
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"""Evaluate an expression with parentheses."""
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while "(" in expression:
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start = expression.rfind("(")
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end = expression.find(")", start)
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sub_exp = expression[start + 1 : end]
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# Replace the evaluated part with a placeholder and then evaluate it
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sub_result = evaluate_simple_expression(sub_exp)
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# For simplicity in handling, join sub-results with OR to reprocess them later
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expression = (
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expression[:start] + "|".join(sub_result) + expression[end + 1 :]
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)
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return evaluate_simple_expression(expression)
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result = evaluate_expression(expression)
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if not result:
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raise ValueError("No state keys matched the expression.")
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# Remove redundant state keys from the result, without changing their order
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final_result = []
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for key in result:
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if key not in final_result:
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final_result.append(key)
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return final_result
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